{"id":"https://openalex.org/W4206030306","doi":"https://doi.org/10.1109/access.2021.3134138","title":"Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting","display_name":"Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W4206030306","doi":"https://doi.org/10.1109/access.2021.3134138"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3134138","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3134138","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09643011.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09643011.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050147907","display_name":"Kazi Ekramul Hoque","orcid":"https://orcid.org/0000-0003-1322-8197"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Kazi Ekramul Hoque","raw_affiliation_strings":["Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, 31261, KSA. (e-mail: hoque.ekramul17@gmail.com)"],"raw_orcid":"https://orcid.org/0000-0003-1322-8197","affiliations":[{"raw_affiliation_string":"Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, 31261, KSA. (e-mail: hoque.ekramul17@gmail.com)","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055125044","display_name":"Hamoud Aljamaan","orcid":"https://orcid.org/0000-0002-2146-9348"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Hamoud Aljamaan","raw_affiliation_strings":["Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, 31261, KSA"],"raw_orcid":"https://orcid.org/0000-0002-2146-9348","affiliations":[{"raw_affiliation_string":"Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, 31261, KSA","institution_ids":["https://openalex.org/I134085113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I134085113"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":8.3217,"has_fulltext":true,"cited_by_count":95,"citation_normalized_percentile":{"value":0.98071754,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"163815","last_page":"163830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.8623785972595215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7078776359558105},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6270283460617065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6018113493919373},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.5455254912376404},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.42448049783706665},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.35224074125289917},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.14141243696212769},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.11067533493041992},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0759570300579071}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8623785972595215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7078776359558105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6270283460617065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6018113493919373},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.5455254912376404},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.42448049783706665},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.35224074125289917},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.14141243696212769},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.11067533493041992},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0759570300579071},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3134138","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3134138","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09643011.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a6cc46c443274fc4a3319eca3d50a509","is_oa":true,"landing_page_url":"https://doaj.org/article/a6cc46c443274fc4a3319eca3d50a509","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 163815-163830 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3134138","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3134138","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09643011.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322323","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4206030306.pdf","grobid_xml":"https://content.openalex.org/works/W4206030306.grobid-xml"},"referenced_works_count":84,"referenced_works":["https://openalex.org/W13736609","https://openalex.org/W114517082","https://openalex.org/W130141991","https://openalex.org/W1493879008","https://openalex.org/W1502922572","https://openalex.org/W1532221965","https://openalex.org/W1563088657","https://openalex.org/W1755117326","https://openalex.org/W1825242443","https://openalex.org/W1857789879","https://openalex.org/W1869362176","https://openalex.org/W1955857676","https://openalex.org/W1964357740","https://openalex.org/W1969852690","https://openalex.org/W1981552604","https://openalex.org/W1984147295","https://openalex.org/W1985290879","https://openalex.org/W1986078433","https://openalex.org/W1988608305","https://openalex.org/W1997250767","https://openalex.org/W1998278150","https://openalex.org/W2006487144","https://openalex.org/W2016210396","https://openalex.org/W2075965721","https://openalex.org/W2093349432","https://openalex.org/W2094515478","https://openalex.org/W2094793254","https://openalex.org/W2097998348","https://openalex.org/W2101234009","https://openalex.org/W2107026277","https://openalex.org/W2108832705","https://openalex.org/W2131241448","https://openalex.org/W2135046866","https://openalex.org/W2135093973","https://openalex.org/W2137225583","https://openalex.org/W2149298154","https://openalex.org/W2166107799","https://openalex.org/W2270330859","https://openalex.org/W2489782956","https://openalex.org/W2622999711","https://openalex.org/W2772784825","https://openalex.org/W2784381726","https://openalex.org/W2794916302","https://openalex.org/W2797928606","https://openalex.org/W2799318487","https://openalex.org/W2799635155","https://openalex.org/W2804919585","https://openalex.org/W2811507150","https://openalex.org/W2949886060","https://openalex.org/W2970731663","https://openalex.org/W2975788427","https://openalex.org/W2995179471","https://openalex.org/W2998944925","https://openalex.org/W3005590361","https://openalex.org/W3005756927","https://openalex.org/W3011350824","https://openalex.org/W3026353565","https://openalex.org/W3044629235","https://openalex.org/W3045004532","https://openalex.org/W3083186111","https://openalex.org/W3093992164","https://openalex.org/W3099802519","https://openalex.org/W3100297620","https://openalex.org/W3104887532","https://openalex.org/W3115706629","https://openalex.org/W3118243694","https://openalex.org/W3170550652","https://openalex.org/W4213308398","https://openalex.org/W4229954146","https://openalex.org/W4236137412","https://openalex.org/W4244238212","https://openalex.org/W6600561428","https://openalex.org/W6605312667","https://openalex.org/W6629804754","https://openalex.org/W6637962894","https://openalex.org/W6638306306","https://openalex.org/W6674385629","https://openalex.org/W6675354045","https://openalex.org/W6678911119","https://openalex.org/W6680302564","https://openalex.org/W6767009316","https://openalex.org/W6768464860","https://openalex.org/W6771658506","https://openalex.org/W6773229893"],"related_works":["https://openalex.org/W2602382373","https://openalex.org/W3003615511","https://openalex.org/W4285827128","https://openalex.org/W3198113463","https://openalex.org/W2787698406","https://openalex.org/W2963844355","https://openalex.org/W4361251046","https://openalex.org/W98577079","https://openalex.org/W247222457","https://openalex.org/W1488120909"],"abstract_inverted_index":{"Stock":[0,70],"price":[1,42,62],"forecasting":[2,125,142,154,168,178,193],"has":[3],"been":[4],"reported":[5],"as":[6],"a":[7,157],"challenging":[8],"task":[9],"in":[10,78,141],"the":[11,60,68,73,112,147,187],"scientific":[12],"and":[13,21,36,99,120,183],"financial":[14],"communities":[15],"due":[16],"to":[17,32,58,67,110,175],"stock":[18,41,61,143],"prices\u2019":[19],"nonlinear":[20,34],"dynamic":[22],"nature.":[23],"Machine":[24],"learning":[25,54,81,123,139],"models":[26,140,155,189],"exhibit":[27],"capabilities":[28],"that":[29],"allow":[30],"them":[31],"handle":[33],"data":[35],"be":[37],"candidate":[38],"tools":[39],"for":[40],"forecasting.":[43],"In":[44,161],"this":[45],"study,":[46],"an":[47],"empirical":[48],"evaluation":[49],"of":[50,63,76,114,134,137],"eight":[51],"conventional":[52],"machine":[53,80,122,138],"models\u2019":[55,124],"is":[56,83,87,108],"conducted":[57],"forecast":[59],"eleven":[64],"companies":[65],"belonging":[66],"Saudi":[69],"Exchange.":[71],"Moreover,":[72],"optimal":[74],"configuration":[75],"hyperparameters":[77],"each":[79],"model":[82],"identified.":[84],"Forecasting":[85],"performance":[86,169,194],"evaluated":[88],"by":[89,117],"two":[90],"well-known":[91],"error":[92],"metrics:":[93],"Root":[94],"Mean":[95,100],"Square":[96],"Error":[97,103],"(RMSE)":[98],"Absolute":[101],"Percentage":[102],"(MAPE).":[104],"Wilcoxson":[105],"effect":[106],"size":[107],"utilized":[109],"determine":[111],"impact":[113],"hyperparameter":[115,135,171,197],"tuning":[116,136,146,172],"comparing":[118],"tuned":[119],"un-tuned":[121,177],"performance.":[126],"Empirical":[127],"results":[128],"indicate":[129],"there":[130],"are":[131,186],"varying":[132],"impacts":[133],"price.":[144],"After":[145],"hyperparameters,":[148],"Support":[149],"Vector":[150],"Regression":[151,165],"outperforms":[152],"other":[153,176],"with":[156,173],"significant":[158],"statistical":[159],"difference.":[160],"contrast,":[162],"Kernel":[163],"Ridge":[164],"shows":[166],"noteworthy":[167],"without":[170],"respect":[174],"models.":[179],"However,":[180],"Decision":[181],"Tree":[182],"K-Nearest":[184],"Neighbour":[185],"poor-performing":[188],"which":[190],"demonstrate":[191],"inadequate":[192],"even":[195],"after":[196],"tuning.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":37},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}